basics of digital image processing
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Basics of digital image processing. Erkki Rämö. Digital image processing. Editing and interpreting of picture information Examples: Improving the visual quality of the image Removing an error from the image Automated interpretation of the image. Related disciplines. Group discussion 1. - PowerPoint PPT PresentationTRANSCRIPT
BASICS OF DIGITAL IMAGE PROCESSINGErkki Rämö
Digital image processing
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Editing and interpreting of picture information Examples: Improving the visual quality of the
image Removing an error from the image Automated interpretation of the
image
Related disciplines
Group discussion 1• Discuss application areas of digital image processing.
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Where is image processing applied?
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Biological research – cell studies
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Military research – interpretation of reconnaissance photos
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Document control – scanning, interpretation, archiving
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Industry automation – machine vision
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Forensics – Fingerprint analysis
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Medicine – x-ray image analysis
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Photography – Digital photography
Publishing
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Space investigation
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Remote Sensing
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Mapping (eg. Google street view)
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Film industry
Visual image
Light = electromagnetic radiationDifferent wavelengths of light reflect from the object and absorb to the object in different ways, depending on objects surfaces construction and material
Reflecting light is perceived with the eye-brain visual system as an image
Wavelength of visual light is 400 - 700 nm
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Perceiving of the visual image
What is needed:Light source
Light bulb radiates light of some color
Targetwhich reflects part of the light and absorbs the rest
Eye receives the signal signal is interpreted by brain
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10-6 10 103 109
Cosmicrays
Gammarays
X-rays
UVInfra-red
Micro-wave
Radio
nm
Visible light
400 nm 700 nm
Spectrum of light
Group discussion2• List imaging applications working in different wavelengths.• Can you find imaging using else than electromagnetic
radiation
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Eyesight
Eye, visual nervetrack and brains visual centre form the human visual system
There’s no visual system better than the eye Some animal eyes are better than human
eye Examples of ‘analog’ image processing
A paddle in the water, refraction of light in the interface of two substances
Image restoration by eyeglasses
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From optical image to a digital image
The construction of the eye
Cross-section of the human eye
Comparison between an eye and a cameraSimilarities:
In the eye image is drawn upside down to the retinaPupil works like the iris of the cameraRetina, with two types of visual cells, rods (about 120
million) and cones(about 7 million)
Differencies:Focus by changing the refraction of the lense by means
of the radial deformation
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Visual cells of the eye
Rodsthousands of times more sensitive than cones. responsible of dark vision
ConesResponsible of seeing the colors Three kinds: sensitive for blue-purple, green and red-
yellow. Peaks of sensitivity are in the wavelengths of 447 nm,
540 nm ja 577 nm
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Anatomy of the eyeIn the area of accurate sight, in the middle of the yellow-
spots fovea there are no rods but plenty of cones.Outside the fovea, accuracy of vision is poor
5° from the fovea – only half of the accuracyOnly a small area of field of vision is seen accurateMoving the eyeball we can focus on different details
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Anatomy of the eye 2
Sensitivity of visual cells to alteration of lighting is logarithmicWebers law
JND=K*I Where K is constant and JND (Just Noticable Difference)
Example: 100 W lighting 10 W power increment.
In 1000W lighting we need 100W increment for same resultImage: Intensity must be doubled to notice the same
visual difference
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Visual cells react with one anotherMach Band Effect
Eye works like a high pass filter sharpening the detailsOn the edge of the tone slope, dark color seems lighter and
light color seems darker
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Influence of the background Simultaneous contrast
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Influence of the background Simultaneous contrast
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Frequency responseHow small details are still visible?Influences:
Number and positioning of cells, elasticity of the eye, brain response, intensity of light
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Procedure classes of image processing
Procedures have been developed already in1960’s, though due to lack of computing power they were hard to implement
Some procedures enhance the quality of the imageOthers pick and analyze information from the image
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5 Procedure classes
1. Image Enhancement
2. Image Restoration
3. Image Analysis
4. Image Compression
5. Image Synthesis
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Image Enhancement
Most common procedure classCan be used as independent enhancement method or
as pre-operation for other methods, for example reducing the image before analysis
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Image enhancement 2
Goal is to enhance the visual quality of the imagecontrast and brightnessnoise reductionsharpening
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Example: adjusting contrast
Photoshop ”autolevels”, which implements the whole tone scale for the image
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Image Restoration
Goal is to restore an image as original or removal of known photographic errorCorrections:
• Removal of geometric distortion• Removal of blur• Noise removal• Motion-blur removal
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Example: enhancing sharpnessPhotoshop ”Unsharp mask”
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Image AnalysisAs result there rarely is an image, but information about
what’s in the imageImplemented in various tasks involving artificial vision
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Example: Measuring of an object
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Image compressionGoal is to compress image-information so that it would consume less space
Pros needs less space faster transfer
Methods:lossless compression(max 2:1)lossy compression(max 100:1)
Based on redundant information in the image
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Example: JPEG-compression
183 KB 17 KB
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Image SynthesisImage is built out of other images orVisualization of non-image informationUsed when:
taking a picture is not physically possible fast and/or slow events
modelling an object which does not exist
Examples:2D images of projection images mathematicallyvisualization of chart information as an image
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Construction of image processing application
Application can be divided into unit tasks
• Application construction:
Applications
Fundamental Classes
Operations
Process
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Basic description of application Example application:
Capture video image of cars licence plate Process and interpret the signs on the plate Check register if the vehicle has any offense
Image processing part
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Process image and identify letters and numbers as an array
In short: Read the signs of the licence plate
Process classes
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Divide application into unit tasks Image enhancing: Improve the image
quality Image analysis: Interpret the letters and
numbers of the plate
ZHO-408
Operations48
Image enhancement: Improve the image quality Contrast alteration: steepen the contrast Edge highlighting: Outlines of signs
Image analysis: Interpretation of the letters and numbers of the plate Detaching edges: Follow the outlines Classification of objects: Fit vectors into images in
model library
Methods
Contrast alteration: steepening of contrastContrast stretching as pixel operation
Edge highlighting: Outlines of symbolsSobels edge highlighting algorithm
Finding edges: Follow the outlines Edge finding algorithm
Classification of vectors: Fit vectors into images in model library
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